# Make sure your agent holds up in the edge cases too.

> LLM evaluations and guardrails for AI agents: automated quality scoring, LLM judges and systematic testing against misuse, prompt injection and edge cases.

Page: https://semantly.fi/en/evals-and-guardrails  
Language: English  
Also in: [Suomi](https://semantly.fi/evalit-ja-guardrailit.md)

Automated evals score every response against your own metrics. Guardrail testing makes sure the agent doesn't leak data or give in to misuse.

## Typical delivery

- Evaluation criteria and KPIs
- A test set built from real conversations
- Automated response scoring
- Guardrail test report with prioritised findings

## Quality measured, not guessed.

Response quality is scored against your business metrics instead of gut feeling or random spot checks.

### Prompt evaluation and KPI scoring

Automated evals for every response: relevance, groundedness, tone and your own business metrics.

### Test datasets

A dataset built from real conversations that every change can be run against.

### Regression testing

Every prompt and model change is verified with evals before it reaches production.

### Guardrail testing

Systematic testing for misuse and data leakage before real users can trigger them.

Tags: Prompt injection, PII protection, Jailbreak attempts

### Prompt tuning

Iteration based on measured results. You see how each change affects quality scores before release.

### Alerts

Get notified the moment quality drops or a guardrail fires in production.

## Quality at a glance.

Every eval and test is logged to the same dashboard. You see how many requests the agent has handled, what happened along the way and how many of them have been scored, day by day.

- **Everything in one view.** Evals, guardrail tests and user feedback are logged to the same dashboard, right next to your traces.
- **Trends over time.** See how usage and quality scores change day by day, and whether your latest change improved quality.
- **Anomalies stand out.** Spikes and dips stand out in the chart, and you can drill straight down into individual traces.

What the example shows: the mid-October usage spike produced almost no quality scores because automated evals weren't running yet. By the late-October spike, nearly every trace was being scored.

## The right eval for every situation.

Not everything can or should be measured the same way. We combine three types of evals, depending on what each criterion needs and how much an evaluation may cost.

### Code-based evals (Deterministic)

Checks written as code for things that are clear-cut: is the response in the right format, did the agent call the right tool with the right parameters, and does the answer stay within agreed limits.

They are fast, nearly free and always reproducible, which is why they run on every response.

### LLM-as-a-judge (Qualitative)

A language model grades the agent's response against a written rubric: is it relevant, is it grounded in the sources, and does it follow the tone and instructions.

Ideal for criteria you can't write down as a rule. We calibrate the judge against human ratings so you can trust the scores.

### Decision-model evals (Business logic)

When the agent makes decisions (approving, rejecting, classifying or routing), we compare its outcome with a decision model that defines the correct result, such as a decision table or a rules engine.

You see how often the agent reaches the same outcome as the agreed process, and in which cases it deviates.

## How we work.

A phased delivery where every phase has a clear outcome.

1. **Criteria.** Together we agree on what a good answer means for your business.
2. **Test set.** The dataset is built from real conversations and edge cases.
3. **Scoring and guardrail tests.** We automate the evals and run the attack scenarios end to end.
4. **Continuous monitoring.** Scoring runs in production and alerts you to anomalies.

## Other services

- [From pilot to production](https://semantly.fi/en/pilots-to-production.md): A partner that takes your AI pilot reliably into production, with quality, security and monitoring in place.
- [MCP servers and MCP Apps](https://semantly.fi/en/mcp-servers.md): Your product's data and actions inside AI assistants, with interactive views.
- [Agent observability](https://semantly.fi/en/agent-observability.md): Tracing, analytics and dashboards: see what your agent does and what it costs.

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Semantly (Screenful Oy, Helsinki). Contact: hey@semantly.fi · [Book an assessment call](https://semantly.fi/en/contact)